Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti.
Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti.
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DOI：
10.1371/journal.pmed.1001083
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发表时间：
2011-08
期刊：
影响因子：
15.8
通讯作者：
von Schreeb J
中科院分区：
文献类型：
--
作者：
Bengtsson L;Lu X;Thorson A;Garfield R;von Schreeb J
林纳斯·本特松及其同事研究了利用手机定位数据监测灾害和疫情期间人口流动的情况，发现可在收到数据后的12小时内生成人口流动报告。
灾害后的人口流动会导致发病率和死亡率大幅上升。如果不了解受灾人群的位置，救援工作就会受阻。目前还没有快速、准确的方法来追踪灾害后的人口流动。我们利用海地最大的移动电话公司（Digicel）的用户识别模块（SIM）卡的位置数据，来估算2010年海地地震和霍乱疫情爆发后人口流动的规模和趋势。
SIM卡的地理位置是由其每次通话时所连接的手机信号塔的位置确定的。我们追踪了地震前42天和地震后158天内SIM卡的每日位置。为了排除未激活的SIM卡，我们只纳入了在地震前和研究的最后一个月内至少拨打过一次电话的190万张SIM卡。在太子港，每张纳入研究的SIM卡对应3.2人。我们利用这个比例从移动的SIM卡数量推算出移动的人口数量。霍乱疫情分析涵盖了8天，追踪了138,560张SIM卡。
据估计，地震当天在太子港的63万人（197,484张Digicel SIM卡）在地震后19天已经离开。估计的人口净流出量（流出量减去流入量）相当于太子港地震前人口的20%。从太子港流出的人口的地理分布与联合国一项大型回顾性、基于人口的调查结果非常吻合。为了证明快速估算的可行性，并确定疫情爆发风险可能增加的地区，我们在霍乱疫情爆发地区疫情刚出现时以及收到数据后的12小时内就生成了关于SIM卡流动的报告。
结果表明，在手机使用率高的地区，可以快速且可能高效地提供灾害和疫情期间人口流动的估算。
请查看文章后文的编辑总结
每年，数百万人受到灾害的影响——这些突发的灾难性事件扰乱社区，造成重大的人员、物质、经济和环境损失。灾害可以是自然的（例如地震和传染病疫情爆发），也可以是人为的（例如恐怖袭击和工业事故）。每当灾害发生时，政府、国际机构和人道主义机构都会立即行动起来，通过提供食物、水、住所和医疗援助来帮助受灾人口。例如，在2010年1月12日海地发生地震后的几天内，许多政府承诺提供大笔资金帮助海地人，乐施会和红十字会与红新月会国际联合会等人道主义机构向该国运送了数吨食物和数百名工作人员。2010年10月海地爆发霍乱时，世界各国也纷纷提供进一步的援助。
对任何灾害的本能反应都是逃离受灾地区，但灾害后的这种人口流动会因使救援援助的提供、需求评估和传染病监测变得复杂而增加人员生命损失。不幸的是，目前没有快速或准确的方法来追踪灾害后的人口流动。救援协调员目前依靠缓慢且可能有偏差的方法，如目击者的描述和避难所的航拍图像来追踪人口流动。在这项地理空间分析中，研究人员通过回顾性追踪海地地震前后SIM卡的位置以及在霍乱疫情爆发的最初几天追踪SIM卡，来研究手机SIM（用户识别模块）的位置数据是否可用于估算人口流动的规模和趋势。每次SIM卡通话时，移动电话网络数据库都会记录是哪个手机信号塔连接了该通话。因此，该数据库可以为每个移动电话呼叫者提供地理位置。
研究人员获取了海地190万张SIM卡从地震前42天到地震后158天的匿名位置数据。地震发生时在海地首都太子港的近20万张SIM卡在地震后19天已经离开。地震发生时，太子港近三分之一的居民是手机用户，所以这些SIM卡的流动相当于约63万人的流动。值得注意的是，尽管基于SIM卡的离开太子港人数的估算与海地国家民防局（NPCA）报告的估算相符，后者主要基于对船只和公共汽车流动的统计，并在救援行动中使用，但NPCA报告的流离失所者的估计地理分布与通过分析SIM卡流动所获得的结果有很大不同。相比之下，从SIM卡流动中获得的人口地理分布与联合国人口基金一项回顾性家庭调查所报告的结果非常吻合。最后，为了证明在灾害期间快速估算人口流动的可行性，研究人员在海地霍乱疫情爆发的前8天追踪了近14万张SIM卡，并表明他们可以在从移动电话公司收到数据后的12小时内发布SIM卡流动分析结果。
这些发现表明，通过分析手机数据，可以在手机使用率高的地区快速、准确地提供灾害和传染病疫情爆发期间人口流动的估算。现在全球86%的人口拥有移动电话网络覆盖，2009年，发展中国家已经有32亿移动电话用户，而发展中国家人口为55亿。因此，这种追踪方法在世界许多地区可能有用，包括那些特别容易遭受灾害的地区。然而，由于社会各阶层的手机使用情况不同，一些地区的手机信号塔密度较低，而且灾害可能会摧毁这些信号塔，所以这种方法可能并非在所有灾害中都有效。因此，研究人员建议进一步评估利用手机数据追踪人口流动的方法，并与移动电话公司建立联系，以确保在未来发生灾害后能够迅速实施这种方法。
请通过本总结的在线版本访问这些网站：http://dx.doi.org/10.1371/journal.pmed.1001083
国内流离失所问题监测中心提供自然灾害期间人口流离失所的信息
红十字会与红新月会国际联合会提供多种语言的关于灾害管理以及2010年海地地震的信息
乐施会也有关于冲突和自然灾害以及海地地震和霍乱疫情（多种语言）的信息
国际灾害数据库（EM - DAT）包含1990年至今世界上18,000起大规模灾害的基本核心信息
Linus Bengtsson and colleagues examine the use of mobile phone positioning data to monitor population movements during disasters and outbreaks, finding that reports on population movements can be generated within twelve hours of receiving data. Population movements following disasters can cause important increases in morbidity and mortality. Without knowledge of the locations of affected people, relief assistance is compromised. No rapid and accurate method exists to track population movements after disasters. We used position data of subscriber identity module (SIM) cards from the largest mobile phone company in Haiti (Digicel) to estimate the magnitude and trends of population movements following the Haiti 2010 earthquake and cholera outbreak. Geographic positions of SIM cards were determined by the location of the mobile phone tower through which each SIM card connects when calling. We followed daily positions of SIM cards 42 days before the earthquake and 158 days after. To exclude inactivated SIM cards, we included only the 1.9 million SIM cards that made at least one call both pre-earthquake and during the last month of study. In Port-au-Prince there were 3.2 persons per included SIM card. We used this ratio to extrapolate from the number of moving SIM cards to the number of moving persons. Cholera outbreak analyses covered 8 days and tracked 138,560 SIM cards. An estimated 630,000 persons (197,484 Digicel SIM cards), present in Port-au-Prince on the day of the earthquake, had left 19 days post-earthquake. Estimated net outflow of people (outflow minus inflow) corresponded to 20% of the Port-au-Prince pre-earthquake population. Geographic distribution of population movements from Port-au-Prince corresponded well with results from a large retrospective, population-based UN survey. To demonstrate feasibility of rapid estimates and to identify areas at potentially increased risk of outbreaks, we produced reports on SIM card movements from a cholera outbreak area at its immediate onset and within 12 hours of receiving data. Results suggest that estimates of population movements during disasters and outbreaks can be delivered rapidly and with potentially high validity in areas with high mobile phone use. Please see later in the article for the Editors' Summary Every year, millions of people are affected by disasters—sudden calamitous events that disrupt communities and cause major human, material, economic, and environmental losses. Disasters can be natural (for example, earthquakes and infectious disease outbreaks) or man-made (for example, terrorist attacks and industrial accidents). Whenever a disaster strikes, governments, international bodies, and humanitarian agencies swing into action to help the affected population by providing food, water, shelter, and medical assistance. Within days of the earthquake that struck Haiti on January 12, 2010, for instance, many governments pledged large sums of money to help the Haitians, and humanitarian agencies such as Oxfam and the International Federation of Red Cross and Red Crescent Societies sent tons of food and hundreds of personnel into the country. And when a cholera outbreak began in Haiti in October 2010, the world responded by sending further assistance. An instinctive response to any disaster is to flee the affected area, but such population movements after a disaster can increase the loss of human life by complicating the provision of relief assistance, the assessment of needs, and infectious disease surveillance. Unfortunately, there are no rapid or accurate methods available to track population movements after disasters. Relief coordinators currently rely on slow, potentially biased methods such as eye witness accounts and aerial images of shelters to track population movements. In this geospatial analysis, the researchers investigate whether position data from mobile phone SIMs (subscriber identity modules) can be used to estimate the magnitude and trends of population movements by retrospectively following the positions of SIMs in Haiti before and after the earthquake and tracking SIMs during the first few days of the cholera outbreak. Every time a SIM makes a call, the mobile phone network database records which mobile phone tower connected the call. Thus, the database can provide a geographic position for each mobile phone caller. The researchers obtained anonymized data on the position of 1.9 million SIMs in Haiti from 42 days before the earthquake to 158 days afterwards. Nearly 200,000 SIMs that were present in Haiti's capital Port-au-Prince when the earthquake struck had left 19 days post-earthquake. Just under a third of Port-au-Prince's inhabitants were mobile phone subscribers at the time of the earthquake, so this movement of SIMs equates to the movement of about 630,000 people. Notably, although the SIM-based estimates of numbers leaving Port-au-Prince matched the estimates reported by the Haitian National Civil Protection Agency (NPCA), which were largely based on counting ship and bus movements and which were used during the relief operation, the estimated geographical distribution of displaced people reported by the NPCA was very different to that obtained by analyzing SIM movements. By contrast, the geographical distribution of the population obtained from SIM movements closely matched that reported by a retrospective United Nations Population Fund household survey. Finally, to demonstrate the feasibility of producing rapid estimates of population movements during disasters, the researchers tracked nearly 140,000 SIMs during the first 8 days of the Haitian cholera outbreak and showed that they could distribute analyses of SIM movements within 12 hours of receiving data from the mobile phone company. These findings suggest that estimates of population movements during disasters and infectious disease outbreaks can be delivered rapidly and accurately in areas of high mobile use by analyzing mobile phone data. 86% of the world's population now has mobile phone network coverage and, in 2009, there were already 3.2 billion mobile phone subscriptions in the developing world, which has a population of 5.5 billion people. Thus, this tracking method could be useful in many parts of the world, including those particularly vulnerable to disasters. However, because mobile phone use varies between sections of society, because some areas have a low density of mobile phone towers, and because disasters can destroy these towers, this approach may not be effective in all disasters. The researchers recommend, therefore, that the use of mobile phone data for tracking population movements is evaluated further and that relationships are built up with mobile phone companies to ensure rapid implementation of the approach after future disasters. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001083. Internal Displacement Monitoring Centre provides information on population displacement during natural disasters The International Federation of Red Cross and Red Crescent Societies provides information in several languages about disaster management and about the 2010 earthquake in Haiti Oxfam also has information on conflicts and natural disasters and the Haiti earthquake and cholera outbreak (in several languages) EM-DAT, the International Disaster Database contains essential core information on 18,000 mass disasters in the world from 1990 until the present
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2009-12-22
影响因子：
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